AI agents are safer when they cannot act on a model’s judgment alone. Give each agent only the tools and permissions its task requires, enforce authorization in application code or policy controls, treat retrieved content as untrusted, and put approval gates around consequential actions. Then validate, log, and adversarially test the system. A prompt can guide an agent, but it should not be the only thing preventing an unauthorized or harmful operation.
Why an agent needs more than a careful prompt
An AI agent can do more than produce text: it may retrieve web pages or documents, call tools, change records, or trigger actions in connected services. That creates a security boundary between what the model proposes and what the surrounding system permits. A well-worded instruction may reduce mistakes, but it cannot reliably serve as the permission system. The model can misunderstand a request, follow malicious instructions embedded in retrieved material, or produce an action that exceeds the intended scope.
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A safer design treats the model as one component in a controlled workflow. The model can interpret a task and propose a tool call; a separate application or policy layer checks whether the caller is authorized, whether the requested operation is allowed, whether its arguments are within scope, and whether approval is required. Only then does an execution component perform the action. If a check fails, the action should be denied rather than relying on the model to reconsider.
This separation matters most when an agent can write data, affect access, spend money, communicate externally, or make changes that are difficult to reverse. The more authority an agent has, the more important it is to place enforceable controls between its reasoning and execution.
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Limit the agent’s authority before deployment
Grant only task-specific permissions
Start by listing the tools and resources the task actually needs. Give the agent the narrowest useful scope, such as access to a particular set of records rather than an entire account, or permission to read information without permission to modify it. If the job is reporting, a write-capable tool is unnecessary authority. If a task needs one kind of change, do not also grant permission to alter user access or system-wide configuration.
Permission should be constrained at the tool and service boundary, not merely described in the system prompt. The application should authenticate the acting user or service, check that identity’s rights for the requested resource, and deny calls outside those rights. OWASP’s agent guidance says agents should operate under administrator change-management controls with additional automated guardrails, and calls for explicit approval for security-relevant changes.
Separate proposing from executing
Design tool use so that an agent’s proposed action is not equivalent to an executed action. A policy or execution component should independently check the operation, target, arguments, privilege level, and approval state. This is especially important when the agent’s own context may include untrusted material. A model can recommend an action; it cannot grant itself authority to take it.
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Choose controls by the risk of each action
Not every tool call needs the same friction. Classify actions using four questions: How much impact could an error have? Can the action be reversed? What privilege does it require? Could untrusted external content have triggered it? Use the answers to decide whether an operation can happen automatically, needs a person’s approval, or should be unavailable to the agent.
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| Action characteristics | Suitable control | Illustrative example |
|---|---|---|
| Low impact, reversible, narrow scope | Allowlisted action with application-level authorization and bounded arguments | Read a permitted record or prepare a draft without sending it |
| Meaningful impact or difficult to reverse | Show the proposed operation and exact arguments to an authorized reviewer before execution | Modify a customer record or send an external message |
| Security-relevant, highly privileged, or destructive | Require strong independent authorization and additional integrity controls; prohibit agent execution if adequate controls are unavailable | Change access rights, alter system configuration, or perform a destructive operation |
| Triggered by untrusted retrieved content and consequential | Do not let the content authorize the action; require a separate trusted request or human confirmation | Act on an instruction found in a document, email, or web page |
These examples are a decision aid, not a universal risk rating. The same operation can have different consequences depending on the data, user, and environment. Set thresholds with the people responsible for the service and its security, and make the enforcement rule explicit.
Keep retrieved content from becoming an instruction
Prompt injection can be direct, or indirect: an agent may encounter malicious or misleading instructions inside a page, document, email, or tool response it was asked to inspect. If that content is treated as authority, it can redirect the agent away from the user’s task or try to induce an unsafe tool call. Retrieval does not make content trustworthy.
Keep trusted instructions and external content distinguishable in the agent’s context and in the application’s handling of tool results. Tell the model that retrieved material is data to analyze, not a source of permission. More importantly, enforce the distinction outside the model: tool output must not change the agent’s access rights, expand its allowed actions, or satisfy an approval requirement.
OWASP Cornucopia, Agentic AI card AAI7, states: “Tool output must be treated as untrusted external data and handled with the same caution applied to any other user-supplied input.” In practice, this means validating tool results, constraining what actions can follow from them, and treating instructions found in external content as potentially hostile even when the source appears ordinary.
Monitor whether a proposed action still fits the original task and authorized scope. If untrusted content appears to drive a high-impact operation, stop for review rather than allowing the agent to interpret that content as consent. Action allowlists and narrow resource scopes help contain the impact if an injection attempt succeeds in influencing the model’s reasoning.
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Put human approval at the execution boundary
Human review is useful only if it is tied to the action that will actually happen. A broad prompt asking a model whether an operation is safe is not an approval control. Nor is a generic “approve this agent” step that leaves the reviewer unaware of the operation’s target and parameters.
For a gated action, present the reviewer with the proposed operation, the target resource, the relevant arguments, and the identity or authority under which it will run. The execution layer should verify that approval applies to that exact action. If the target or arguments change after approval, treat it as a different action and require the appropriate review again. Record the approval identifier and connect it to the eventual execution result.
Use approval for actions that are high-impact, irreversible, security-relevant, or externally visible. For destructive, financial, administrative, or externally visible operations, OWASP’s Cheat Sheet recommends additional integrity controls beyond a simple approval prompt. The particular controls depend on the operation, but the principle is consistent: an approval screen should not be the sole barrier protecting a sensitive capability.
Decide in advance what happens when a reviewer denies an action, does not respond, or cannot be reached. The safe default for a consequential action is not to proceed. The agent may be able to offer a lower-risk alternative, such as preparing a draft or report, but it should not silently retry through another tool or change the request to bypass the gate.
Validate outputs before displaying or executing them
Model output is not automatically safe simply because it is syntactically valid or confidently phrased. Before passing an output to a tool, check that it matches the expected schema, uses an allowed action, and stays within limits on targets, arguments, rate, and scope. Reject malformed, incomplete, or out-of-range requests. A schema can make the interface more predictable, but it does not replace authorization: a perfectly formed request can still be disallowed.
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Apply validation to user-facing output as well as tool calls when the agent handles sensitive material or produces content that will be shown to others. Separate a draft from a sent message, and a proposed change from a committed one. Where possible, use bounded operations rather than unrestricted commands, so that a faulty or manipulated output cannot choose arbitrary resources or actions.
Validation should be performed by the component that controls the next step. If a tool call will modify a record, validate before the write. If the output will be displayed externally, apply the relevant checks before publication. Returning an error to the agent can support recovery, but the error response must not itself grant expanded permissions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Log decisions without collecting unnecessary sensitive data
Auditing helps an organization reconstruct what the agent attempted and whether controls worked. Record security-relevant decisions such as the requested operation, authorization outcome, approval reference, policy version, execution result, and whether the action was denied or timed out. Logs should make it possible to connect a proposal to its approval and final result.
Keep logs proportionate. Do not put credentials, secrets, or unnecessary sensitive content into a log simply because it is convenient for debugging. Define who can access audit records and how long they are retained according to the system’s needs and applicable requirements. Monitoring should surface out-of-scope attempts, repeated denials, unexpected action expansion, and approval or execution failures for review.
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Test the controls against adversarial behavior
Test an agent before production with cases that challenge the boundary between instructions, data, and authority. Include direct and indirect prompt injection, attempts to misuse tools, memory poisoning, and approval bypass. Check not only whether the model refuses an unsafe request, but whether the application independently blocks it when the model does not.
Use instrumented substitutes for consequential services where appropriate, so tests can observe proposed calls and denial behavior without causing real changes. Verify that an approval is required for the intended action, that changing the target or arguments invalidates a prior approval, and that denial, timeout, or circuit-breaker behavior fails safely. Preserve the tested version, abuse cases, and observed outcomes.
Testing is not a one-time launch gate. Repeat it after material changes to prompts, tools, memory, retrieval, policies, or model providers, because each can change how the agent behaves or what it can reach. Include regression cases for controls that previously prevented unsafe actions. A change that appears to improve task performance can also affect the system’s security boundary.
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What current NIST work says—and does not say
In a May 18, 2026 summary of responses to its request for information, NIST reported broad agreement among commenters that agents present novel security threats and that these concerns are a barrier to adoption. Respondents also said foundational cybersecurity practices need adaptation for agents. This is a summary of what commenters said, not a measured prevalence estimate or a quantified level of risk.
On February 5, 2026, NIST announced a concept paper identifying software-agent identification, authorization, auditing, and non-repudiation as areas for consideration, along with controls to prevent and mitigate prompt injection. That announcement describes proposed work; it is not a completed certification or finalized standard. These developments reinforce the practical value of identity, authorization, and audit controls, but they do not replace an organization’s own risk decisions or establish that a particular agent is safe.
A practical deployment sequence
- Define the task and boundaries. Write down what the agent must accomplish, which resources it may access, and which actions it must never perform.
- Map tools to minimum permissions. Remove unnecessary tools and write access. Scope remaining access to the task and the relevant resources.
- Put enforcement outside the model. Make the application or policy layer check caller identity, permissions, action scope, and approval status before execution.
- Mark external material as untrusted. Ensure retrieved content cannot change permissions or authorize actions, and constrain the operations it can influence.
- Set action-specific gates. Decide which operations are automatically allowed, which require exact-action approval, and which are prohibited.
- Validate and instrument. Check schemas and limits before execution; log authorization, approval, policy, and outcome information without unnecessary secrets.
- Exercise failure cases and retest changes. Test injection, misuse, denial, timeout, and approval-bypass scenarios, then repeat tests when important system components change.
For each proposed capability, ask whether the agent truly needs it and what happens if the model is wrong or influenced by hostile content. If the answer is unclear, narrow the permission, require a stronger check, or keep the operation out of the agent’s reach.
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